Method and apparatus for resource allocation in wireless communication system

ABSTRACT

An operation method in a communication system may comprise: obtaining information on a per-beam required traffic amount; determining whether the per-beam required traffic amount can be serviced while satisfying a first condition according to a first model generated through pre-training in a first machine learning structure; in response to determining that the per-beam required traffic amount can be serviced while satisfying the first condition, calculating per-beam bandwidth allocation information based on the per-beam required traffic amount; calculating per-beam power allocation information based on the per-beam bandwidth allocation information; identifying whether an SNR condition included in the first condition is satisfied based on the per-beam power allocation information; and in response to identifying that the SNR condition is satisfied, outputting the per-beam bandwidth allocation information and the per-beam power allocation information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Korean Patent Applications No.10-2021-0115034 filed on Aug. 30, 2021, and No. 10-2022-0109202 filed onAug. 30, 2022, in the Korean Intellectual Property Office (KIPO), theentire contents of which are hereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a resource allocation technique for awireless communication system, and more particularly, to a technique forallocating resources such as per-beam bandwidth and power based on amachine learning algorithm in a wireless communication system usingmultiple beams.

2. Related Art

The communication system (hereinafter, a new radio (NR) communicationsystem) using a higher frequency band (e.g., a frequency band of 6 GHzor higher) than a frequency band (e.g., a frequency band lower below 6GHz) of the long term evolution (LTE) (or, LTE-A) is being consideredfor processing of soaring wireless data. The NR communication system maysupport not only a frequency band below 6 GHz but also 6 GHz or higherfrequency band, and may support various communication services andscenarios as compared to the LTE communication system. For example,usage scenarios of the NR communication system may include enhancedmobile broadband (eMBB), ultra-reliable low-latency communication(URLLC), massive machine type communication (mMTC), and the like.

The NR communication network may provide communication services toterminals located in terrestrial sites. Recently, the demand forcommunication services for planes, drones, etc., which are located inthe non-terrestrial places, or the demand for communication servicesthrough satellites is increasing. To this end, techniques for anon-terrestrial network (NTN) are being discussed.

Meanwhile, a frequency reuse technique may be applied to efficientlyprovide services to multiple users using limited radio resources in awireless communication network. For example, when a plurality of beamsor a plurality of cells use the same frequency band, the frequency reusetechnique may be used to mitigate inter-beam interference or inter-cellinterference. In particular, in a communication environment (e.g., NTN,or the like) in which resources such as frequency and power are verylimited, a technique for efficiently allocating resources using thefrequency reuse technique may be required.

Matters described as the prior arts are prepared to promoteunderstanding of the background of the present disclosure, and mayinclude matters that are not already known to those of ordinary skill inthe technology domain to which exemplary embodiments of the presentdisclosure belong.

SUMMARY

Accordingly, exemplary embodiments of the present disclosure provide amethod and an apparatus for resource allocation, which can improve theperformance of allocating resources such as per-beam bandwidth and powerbased on a machine learning algorithm in a wireless communication systemto which a frequency reuse technique is applied.

According to an exemplary embodiment of the present disclosure, anoperation method of a first apparatus in a communication system maycomprise: obtaining information on a per-beam required traffic amount;determining whether the per-beam required traffic amount can be servicedwhile satisfying a first condition including an available totalbandwidth condition and an available total power condition according toa first model generated through pre-training in a first machine learningstructure; in response to determining that the per-beam required trafficamount can be serviced while satisfying the first condition, calculatingper-beam bandwidth allocation information based on the per-beam requiredtraffic amount; calculating per-beam power allocation information basedon the per-beam bandwidth allocation information; identifying whether asignal-to-noise ratio (SNR) condition included in the first condition issatisfied based on the per-beam power allocation information; and inresponse to identifying that the SNR condition is satisfied, outputtingthe per-beam bandwidth allocation information and the per-beam powerallocation information.

The determining may comprise: generating first input data by convertingthe information on the per-beam required traffic amount into a vector;inputting the first input data to the first model; and identifying anoutput value of the first model.

The first machine learning structure may have a perceptron structure,the output value of the first model, which has a positive value, maymean that the per-beam required traffic amount can be serviced whilesatisfying the first condition, and the output value of the first model,which has a negative value, may mean that the per-beam required trafficamount cannot be serviced while satisfying the first condition.

The pre-training in the first machine learning structure may beperformed based on the data obtained from a second model after thesecond model is generated through pre-training in a second machinelearning structure for calculation of the per-beam bandwidth allocationinformation.

The calculating of the per-beam bandwidth allocation information maycomprise: inputting first input data generated based on the informationon the per-beam required traffic amount into a second model generatedthrough pre-training in a second machine learning structure; andobtaining output data from the second model, wherein the output dataincludes the per-beam bandwidth allocation information.

The second machine learning structure may have a machine learningstructure according to a linear regression learning scheme, and thepre-training in the second machine learning structure may be performedbased on the data obtained from an exhaustive search scheme in adirection in which a value of a loss function calculated based on firstbandwidth allocation information output based on information on a firstrequired traffic amount is minimized.

The operation method may further comprise, after the determining, inresponse to determining that the per-beam required traffic amount cannotbe serviced while satisfying the first condition, performing an affineprojection operation for calculating a reduced required traffic amountreduced from the per-beam required traffic amount; and calculating theper-beam bandwidth allocation information based on second input datagenerated as a result of the affine projection operation and includinginformation on the reduced required traffic amount.

The operation method may further comprise, after the identifying, inresponse to identifying that the SNR condition is not satisfied,adjusting a boundary value used in the affine projection operation;performing the affine projection operation based on the adjustedboundary value; and calculating the per-beam bandwidth allocationinformation based on third input data generated as a result of theaffine projection operation performed based on the adjusted boundaryvalue.

Furthermore, according to another exemplary embodiment of the presentdisclosure, a first apparatus in a communication system may comprise: aprocessor; a memory electronically communicating with the processor; andinstructions stored in the memory, wherein when executed by theprocessor, the instructions cause the first apparatus to: obtaininformation on a per-beam required traffic amount; determine whether theper-beam required traffic amount can be serviced while satisfying afirst condition including an available total bandwidth condition and anavailable total power condition according to a first model generatedthrough pre-training in a first machine learning structure; in responseto determining that the per-beam required traffic amount can be servicedwhile satisfying the first condition, calculate per-beam bandwidthallocation information based on the per-beam required traffic amount;calculate per-beam power allocation information based on the per-beambandwidth allocation information; identify whether a signal-to-noiseratio (SNR) condition included in the first condition is satisfied basedon the per-beam power allocation information; and in response toidentifying that the SNR condition is satisfied, output the per-beambandwidth allocation information and the per-beam power allocationinformation.

In the determining, the instructions may further cause the firstapparatus to: generate first input data by converting the information onthe per-beam required traffic amount into a vector; input the firstinput data to the first model; and identify an output value of the firstmodel.

The first machine learning structure may have a perceptron structure,the output value of the first model, which has a positive value, maymean that the per-beam required traffic amount can be serviced whilesatisfying the first condition, the output value of the first model,which has a negative value, may mean that the per-beam required trafficamount cannot be serviced while satisfying the first condition, and thepre-training in the first machine learning structure may be performedbased on the data obtained from a second model after the second model isgenerated through pre-training in a second machine learning structurefor calculation of the per-beam bandwidth allocation information.

In the calculating of the per-beam bandwidth allocation information, theinstructions may further cause the first apparatus to: input first inputdata generated based on the information on the per-beam required trafficamount into a second model generated through pre-training in a secondmachine learning structure; and obtain output data output from thesecond model, wherein the output data includes the per-beam bandwidthallocation information.

The second machine learning structure may have a machine learningstructure according to a linear regression learning scheme, and thepre-training in the second machine learning structure may be performedbased on the data obtained from an exhaustive search scheme in adirection in which a value of a loss function calculated based on firstbandwidth allocation information output based on information on a firstrequired traffic amount is minimized.

The instructions may further cause the first apparatus to, after thedetermining, in response to determining that the per-beam requiredtraffic amount cannot be serviced while satisfying the first condition,perform an affine projection operation for calculating a reducedrequired traffic amount reduced from the per-beam required trafficamount; and calculate the per-beam bandwidth allocation informationbased on second input data generated as a result of the affineprojection operation.

The instructions may further cause the first apparatus to, after theidentifying, in response to identifying that the SNR condition is notsatisfied, adjust a boundary value used in the affine projectionoperation; perform the affine projection operation based on the adjustedboundary value; and calculate the per-beam bandwidth allocationinformation based on third input data generated as a result of theaffine projection operation performed based on the adjusted boundaryvalue.

According to exemplary embodiments of a method and an apparatus forresource allocation in a wireless communication system, in the wirelesscommunication system to which the frequency reuse technique is applied,a first communication node may allocate resources such as bandwidth andpower based on machine learning. The first communication node may obtaininformation on per-beam (or per-cell) required traffic amount, anddetermine whether the required traffic amount can be serviced throughresources such as a system bandwidth and a power available in the firstcommunication node. When it is determined that the required trafficamount cannot be serviced, the traffic amount may be reduced, and aper-beam bandwidth and/or power for servicing the reduced traffic amountmay be determined. Through such the process, an unnecessary amount ofcomputation in the resource allocation process can be reduced, andservice efficiency can be improved.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating a first exemplary embodimentof a non-terrestrial network.

FIG. 2 is a conceptual diagram illustrating a second exemplaryembodiment of a non-terrestrial network.

FIG. 3 is a block diagram illustrating a first exemplary embodiment ofan entity constituting a non-terrestrial network.

FIG. 4 is a conceptual diagram for describing an exemplary embodiment ofa communication system to which a frequency reuse technique is applied.

FIG. 5 is a conceptual diagram for describing an exemplary embodiment ofa machine learning structure used for resource allocation in acommunication system.

FIG. 6 is a conceptual diagram for describing an exemplary embodiment ofa resource allocation apparatus in a communication system.

FIG. 7 is a conceptual diagram for describing an exemplary embodiment ofa resource allocation method in a communication system.

DETAILED DESCRIPTION

Embodiments of the present disclosure are disclosed herein. However,specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing embodiments of the presentdisclosure, however, embodiments of the present disclosure may beembodied in many alternate forms and should not be construed as limitedto embodiments of the present disclosure set forth herein.

Accordingly, while the present disclosure is susceptible to variousmodifications and alternative forms, specific embodiments thereof areshown by way of example in the drawings and will herein be described indetail. It should be understood, however, that there is no intent tolimit the present disclosure to the particular forms disclosed, but onthe contrary, the present disclosure is to cover all modifications,equivalents, and alternatives falling within the spirit and scope of thepresent disclosure. Like numbers refer to like elements throughout thedescription of the figures.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of the present disclosure. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(i.e., “between” versus “directly between,” “adjacent” versus “directlyadjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a,” “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,”“comprising,” “includes” and/or “including,” when usedherein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this present disclosure belongs.It will be further understood that terms, such as those defined incommonly used dictionaries, should be interpreted as having a meaningthat is consistent with their meaning in the context of the relevant artand will not be interpreted in an idealized or overly formal senseunless expressly so defined herein.

Hereinafter, exemplary embodiments of the present disclosure will bedescribed in greater detail with reference to the accompanying drawings.In order to facilitate general understanding in describing the presentdisclosure, the same components in the drawings are denoted with thesame reference signs, and repeated description thereof will be omitted.

A communication network to which exemplary embodiments according to thepresent disclosure are applied will be described. The communicationsystem may be a non-terrestrial network (NTN), a 4G communicationnetwork (e.g., long-term evolution (LTE) communication network), a 5Gcommunication network (e.g., new radio (NR) communication network), orthe like. The 4G communication network and the 5G communication networkmay be classified as terrestrial networks.

The NTN may operate based on the LTE technology and/or the NRtechnology. The NTN may support communications in frequency bands below6 GHz as well as in frequency bands above 6 GHz. The 4G communicationnetwork may support communications in the frequency band below 6 GHz.The 5G communication network may support communications in the frequencyband below 6 GHz as well as in the frequency band above 6 GHz. Thecommunication network to which the exemplary embodiments according tothe present disclosure are applied is not limited to the contentsdescribed below, and the exemplary embodiments according to the presentdisclosure may be applied to various communication networks. Here, thecommunication network may be used in the same sense as the communicationsystem.

FIG. 1 is a conceptual diagram illustrating a first exemplary embodimentof a non-terrestrial network.

Referring to FIG. 1 , a non-terrestrial network (NTN) may include asatellite 110, a communication node 120, a gateway 130, a data network140, and the like. The NTN shown in FIG. 1 may be an NTN based on atransparent payload. The satellite 110 may be a low earth orbit (LEO)satellite, a medium earth orbit (MEO) satellite, a geostationary earthorbit (GEO) satellite, a high elliptical orbit (HEO) satellite, or anunmanned aircraft system (UAS) platform. The UAS platform may include ahigh altitude platform station (HAPS).

The communication node 120 may include a communication node (e.g., auser equipment (UE) or a terminal) located on a terrestrial site and acommunication node (e.g., an airplane, a drone) located on anon-terrestrial place. A service link may be established between thesatellite 110 and the communication node 120, and the service link maybe a radio link. The satellite 110 may provide communication services tothe communication node 120 using one or more beams. The shape of afootprint of the beam of the satellite 110 may be elliptical.

The communication node 120 may perform communications (e.g., downlinkcommunication and uplink communication) with the satellite 110 using LTEtechnology and/or NR technology. The communications between thesatellite 110 and the communication node 120 may be performed using anNR-Uu interface. When dual connectivity (DC) is supported, thecommunication node 120 may be connected to other base stations (e.g.,base stations supporting LTE and/or NR functionality) as well as thesatellite 110, and perform DC operations based on the techniques definedin the LTE and/or NR specifications.

The gateway 130 may be located on a terrestrial site, and a feeder linkmay be established between the satellite 110 and the gateway 130. Thefeeder link may be a radio link. The gateway 130 may be referred to as a‘non-terrestrial network (NTN) gateway’. The communications between thesatellite 110 and the gateway 130 may be performed based on an NR-Uuinterface or a satellite radio interface (SRI). The gateway 130 may beconnected to the data network 140. There may be a ‘core network’ betweenthe gateway 130 and the data network 140. In this case, the gateway 130may be connected to the core network, and the core network may beconnected to the data network 140. The core network may support the NRtechnology. For example, the core network may include an access andmobility management function (AMF), a user plane function (UPF), asession management function (SMF), and the like. The communicationsbetween the gateway 130 and the core network may be performed based onan NG-C/U interface.

Alternatively, a base station and the core network may exist between thegateway 130 and the data network 140. In this case, the gateway 130 maybe connected with the base station, the base station may be connectedwith the core network, and the core network may be connected with thedata network 140. The base station and core network may support the NRtechnology. The communications between the gateway 130 and the basestation may be performed based on an NR-Uu interface, and thecommunications between the base station and the core network (e.g., AMF,UPF, SMF, and the like) may be performed based on an NG-C/U interface.

FIG. 2 is a conceptual diagram illustrating a second exemplaryembodiment of a non-terrestrial network.

Referring to FIG. 2 , a non-terrestrial network may include a firstsatellite 211, a second satellite 212, a communication node 220, agateway 230, a data network 240, and the like. The NTN shown in FIG. 2may be a regenerative payload based NTN. For example, each of thesatellites 211 and 212 may perform a regenerative operation (e.g.,demodulation, decoding, re-encoding, re-modulation, and/or filteringoperation) on a payload received from other entities (e.g., thecommunication node 220 or the gateway 230), and transmit the regeneratedpayload.

Each of the satellites 211 and 212 may be a LEO satellite, a MEOsatellite, a GEO satellite, a HEO satellite, or a UAS platform. The UASplatform may include a HAPS. The satellite 211 may be connected to thesatellite 212, and an inter-satellite link (ISL) may be establishedbetween the satellite 211 and the satellite 212. The ISL may operate inan RF frequency band or an optical band. The ISL may be establishedoptionally. The communication node 220 may include a terrestrialcommunication node (e.g., UE or terminal) and a non-terrestrialcommunication node (e.g., airplane or drone). A service link (e.g.,radio link) may be established between the satellite 211 andcommunication node 220. The satellite 211 may provide communicationservices to the communication node 220 using one or more beams.

The communication node 220 may perform communications (e.g., downlinkcommunication or uplink communication) with the satellite 211 using LTEtechnology and/or NR technology. The communications between thesatellite 211 and the communication node 220 may be performed using anNR-Uu interface. When DC is supported, the communication node 220 may beconnected to other base stations (e.g., base stations supporting LTEand/or NR functionality) as well as the satellite 211, and may performDC operations based on the techniques defined in the LTE and/or NRspecifications.

The gateway 230 may be located on a terrestrial site, a feeder link maybe established between the satellite 211 and the gateway 230, and afeeder link may be established between the satellite 212 and the gateway230. The feeder link may be a radio link. When the ISL is notestablished between the satellite 211 and the satellite 212, the feederlink between the satellite 211 and the gateway 230 may be establishedmandatorily.

The communications between each of the satellites 211 and 212 and thegateway 230 may be performed based on an NR-Uu interface or an SRI. Thegateway 230 may be connected to the data network 240. There may be acore network between the gateway 230 and the data network 240. In thiscase, the gateway 230 may be connected to the core network, and the corenetwork may be connected to the data network 240. The core network maysupport the NR technology. For example, the core network may includeAMF, UPF, SMF, and the like. The communications between the gateway 230and the core network may be performed based on an NG-C/U interface.

Alternatively, a base station and the core network may exist between thegateway 230 and the data network 240. In this case, the gateway 230 maybe connected with the base station, the base station may be connectedwith the core network, and the core network may be connected with thedata network 240. The base station and the core network may support theNR technology. The communications between the gateway 230 and the basestation may be performed based on an NR-Uu interface, and thecommunications between the base station and the core network (e.g., AMF,UPF, SMF, and the like) may be performed based on an NG-C/U interface.

Meanwhile, entities (e.g., satellites, communication nodes, gateways,etc.) constituting the NTNs shown in FIGS. 1 and 2 may be configured asfollows.

FIG. 3 is a block diagram illustrating a first exemplary embodiment ofan entity constituting a non-terrestrial network.

Referring to FIG. 3 , an entity 300 may include at least one processor310, a memory 320, and a transceiver 330 connected to a network toperform communication. In addition, the entity 300 may further includean input interface device 340, an output interface device 350, a storagedevice 360, and the like. The components included in the entity 300 maybe connected by a bus 370 to communicate with each other. However, eachcomponent included in the entity 300 may be connected to the processor310 through a separate interface or a separate bus instead of the commonbus 370. For example, the processor 310 may be connected to at least oneof the memory 320, the transceiver 330, the input interface device 340,the output interface device 350, and the storage device 360 through adedicated interface.

The processor 310 may execute at least one instruction stored in atleast one of the memory 320 and the storage device 360. The processor310 may refer to a central processing unit (CPU), a graphics processingunit (GPU), or a dedicated processor on which the methods according tothe exemplary embodiments of the present invention are performed. Eachof the memory 320 and the storage device 360 may be configured as atleast one of a volatile storage medium and a nonvolatile storage medium.For example, the memory 320 may be configured with at least one of aread only memory (ROM) and a random access memory (RAM).

Meanwhile, scenarios in the NTN may be defined as shown in Table 1below.

TABLE 1 NTN shown in FIG. 1 NTN shown in FIG. 2 GEO Scenario A ScenarioB LEO Scenario C1 Scenario D1 (steerable beams) LEO Scenario C2 ScenarioD2 (beams moving with satellite)

When the satellite 110 in the NTN shown in FIG. 1 is a GEO satellite(e.g., a GEO satellite that supports a transparent function), this maybe referred to as ‘scenario A’. When the satellites 211 and 212 in theNTN shown in FIG. 2 are GEO satellites (e.g., GEOs that support aregenerative function), this may be referred to as ‘scenario B’.

When the satellite 110 in the NTN shown in FIG. 1 is an LEO satellitewith steerable beams, this may be referred to as ‘scenario C1’. When thesatellite 110 in the NTN shown in FIG. 1 is an LEO satellite havingbeams moving with the satellite, this may be referred to as ‘scenarioC2’. When the satellites 211 and 212 in the NTN shown in FIG. 2 are LEOsatellites with steerable beams, this may be referred to as ‘scenarioD1’. When the satellites 211 and 212 in the NTN shown in FIG. 2 are LEOsatellites having beams moving with the satellites, this may be referredto as ‘scenario D2’.

Parameters for the scenarios defined in Table 1 may be defined as shownin Table 2 below.

TABLE 2 Scenarios A and B Scenarios C and D Altitude 35,786 km 600 km1,200 km Spectrum (service link) <6 GHz (e.g., 2 GHz) >6 GHz (e.g., DL20 GHz, UL 30 GHz) Maximum channel 30 MHz for band <6 GHz bandwidthcapability 1 GHz for band >6 GHz (service link) Maximum distance between40,581 km 1,932 km (altitude of 600 km) satellite and communication3,131 km (altitude of 1,200 km) node (e.g., UE) at the minimum elevationangle Maximum round trip delay Scenario A: 541.46 ms Scenario C:(transparent (RTD) (service and feeder links) payload: service andfeeder links) (only propagation delay) Scenario B: 270.73 ms −5.77 ms(altitude of 60 0 km) (only service link) −41.77 ms (altitude of 1,200km) Scenario D: (regenerative payload: only service link) −12.89 ms(altitude of 600 km) −20.89 ms (altitude of 1,200 km) Maximum delayvariation 16 ms 4.44 ms (altitude of 600 km) within a single beam 6.44ms (altitude of 1,200 km) Maximum differential 10.3 ms 3.12 ms (altitudeof 600 km) delay within a cell 3.18 ms (altitude of 1,200 km) Servicelink NR defined in 3GPP Feeder link Radio interfaces defined in 3GPP ornon-3GPP

In addition, in the scenarios defined in Table 1, delay constraints maybe defined as shown in Table 3 below.

TABLE 3 Scenario Scenario Scenario Scenario A B C1-2 D1-2 Satellitealtitude 35,786 km 600 km Maximum RTD in a 541.75 ms 270.57 ms 28.41 ms12.88 ms radio interface (worst case) between base station and UEMinimum RTD in a 477.14 ms 238.57 ms 8 ms 4 ms radio interface betweenbase station and UE

Hereinafter, resource allocation methods for a wireless communicationsystem will be described. Even when a method (e.g., transmission orreception of a signal) to be performed at a first communication nodeamong communication nodes is described, a corresponding secondcommunication node may perform a method (e.g., reception or transmissionof the signal) corresponding to the method performed at the firstcommunication node. That is, when an operation of a terminal isdescribed, a corresponding base station may perform an operationcorresponding to the operation of the terminal. Conversely, when anoperation of the base station is described, the corresponding terminalmay perform an operation corresponding to the operation of the basestation.

FIG. 4 is a conceptual diagram for describing an exemplary embodiment ofa communication system to which a frequency reuse technique is applied.

Referring to FIG. 4 , a communication system may be configured toinclude an NTN and/or a terrestrial network. For example, thecommunication system may include an NTN configured to provide servicesto a predetermined coverage, including one or more satellites and one ormore gateways. Here, the NTN may be configured identically or similarlyto at least one of the first and second exemplary embodiments of the NTNdescribed with reference to FIGS. 1 and 2 . The one or more satellitesand the one or more gateways included in the NTN may be the same as orsimilar to the satellites 110, 211 and 212 and the gateways 130 and 230described with reference to FIGS. 1 and 2 . The NTN may provide servicesto terrestrial communication nodes using multiple beams. Meanwhile, thecommunication system may include the terrestrial network configured toprovide services to a predetermined coverage, including one or moreterrestrial cells. Alternatively, the communication system may be anintegrated satellite and terrestrial (IST) system configured to includethe NTN and the terrestrial network.

In the communication system, a frequency reuse technique may be appliedto efficiently provide services to multiple users using limited radioresources. For example, when a plurality of beams used in the NTN and/ora plurality of cells of the terrestrial network shown in FIG. 4 use thesame frequency band, inter-beam interference and/or inter-cellinterference may occur. As described above, the frequency reusetechnique may be used to mitigate the interference due to the use of thesame frequency band. In particular, in a communication environment(e.g., NTN, or the like) in which resources such as frequency and powerare very limited, it may be advantageous to efficiently allocateresources when the frequency reuse technique is used. The resourceallocation may be performed by a predetermined apparatus (hereinafter,referred to as a ‘resource allocation apparatus’) included in thecommunication system.

In an exemplary embodiment of the communication system, the frequencyreuse technique may be applied in such a manner that the same frequencycan be used in different beams or cells with a geographically sufficientseparation distance. For example, an resource allocation apparatus for asatellite and/or terrestrial cell using a multi-beam may allocatefrequency bands based on a frequency reuse factor F. For example, theresource allocation apparatus may configure a plurality of bandwidthsw₁, w₂, . . . , and w_(F) by dividing an available total frequencybandwidth based on the frequency reuse factor F. The bandwidths w₁, w₂,. . . , and w_(F) may each have a center frequency f₁, f₂, . . . , andf_(F). A satellite using a multi-beam may control coverages of aplurality of beams using bandwidths having the same center frequency tobe geographically separated from each other based on the frequency reusetechnique.

If it is not easy to provide a sufficient separation distance so thatinterference does not occur at all, the frequency reuse technique mayallocate resources such as a minimum power and/or bandwidth to each beamand/or cell so as to easily overcome interference between beams and/orcells using the same frequency, thereby minimizing interference andmaximizing bandwidth efficiency. For example, the resource allocationapparatus may configure an objective function to achieve maximumbandwidth efficiency and minimize interference to other cells or beamswhile satisfying a bit rate or transmission rate required by a user, andfind a solution of a resource allocation based using an optimizationtechnique.

Meanwhile, when the optimization technique is applied to find a solutionof the resource allocation for a system with frequency reusingtechnique, the resource allocation apparatus may obtain the solution ofthe resource allocation amount through a number of iterative operationssuch as an exhaustive search scheme. In this case, there may be aproblem in that it is impossible to know in advance whether a solutionexists before the iterative operations, and even how long it takes tofind a solution in advance even if there is a solution. In order tosolve such the problem, in an exemplary embodiment of the resourceallocation apparatus, a resource allocation operation for finding thesolution of the resource allocation based on a machine learningalgorithm and/or a machine learning structure may be performed. Forexample, the resource allocation apparatus may perform a resourceallocation operation based on the same or similar machine learningstructure to the machine learning structure shown in FIG. 5 .

FIG. 5 is a conceptual diagram for describing an exemplary embodiment ofa machine learning structure used for resource allocation in acommunication system.

Referring to FIG. 5 , a communication system may be the same as orsimilar to the communication system described with reference to FIG. 4 .A resource allocation apparatus of the communication system may performa resource allocation operation for frequency reuse in the communicationsystem based on the same or similar machine learning structure to themachine learning structure shown in FIG. 5 . Hereinafter, in describingan exemplary embodiment of the machine learning structure used forresource allocation in the communication system with reference to FIG. 5, content overlapping with those described with reference to FIGS. 1 to4 may be omitted.

In an exemplary embodiment of the communication system, a computationalmodel for performing a resource allocation operation through a machinelearning structure may be constructed in the resource allocationapparatus. A memory and/or storage device of the resource allocationapparatus may include program instructions for performing machinelearning according to a predetermined machine learning structure.Alternatively, the resource allocation apparatus may include a separatemachine learning unit for performing machine learning according to apredetermined machine learning structure.

The resource allocation apparatus may obtain the computational model forefficiently allocating resources through machine learning according to astructure such as an artificial neural network (ANN) or a deep neuralnetwork (DNN). For example, it can be seen that FIG. 5 shows a DNNstructure configured with multiple layers and multiple nodes amongvarious machine learning structures. However, this is merely an examplefor convenience of description, and exemplary embodiments of thecommunication system are not limited thereto. For example, in anexemplary embodiment of the communication system, various machinelearning structures such as an ANN structure, a recurrent neural network(RNN) structure, a neuron structure consisting of a single node, aperceptron structure consisting of a single node, a knowledge-basedsystem structure, a structure to which a reasoning technique such as aBaysian rule is applied, and a deep neural network structure may beapplied to the machine learning unit. A machine learning structureselected according to a predetermined criterion among various machinelearning structures may be applied to the machine learning unit. Forexample, a machine learning structure selected according to variousconditions such as development and/or production costs, performancerequirements, and processor capability of the communication systemand/or individual devices may be applied to the machine learning unit.

In an exemplary embodiment of the communication system, a plurality oflayers constituting an artificial neural network may include an inputlayer, a hidden layer(s), an output layer, and the like. The input layermay be a layer to which a data set or data group to be learned is input.The input layer may include at least one or more input nodes. Some orall of entries constituting the data set may be input to each of the atleast one or more input nodes constituting the input layer. The data setinput to at least one or more input nodes constituting the input layermay be data that has undergone data preprocessing in advance. The outputlayer may refer to a layer in which data or signals input to theartificial neural network are output through operations in theartificial neural network. The output layer may include at least one ormore output nodes.

At least one or more hidden layers may be disposed between the inputlayer and the output layer. An artificial neural network having two ormore hidden layers may be referred to as a deep neural network (DNN).That is, in a neural network structure including an input layer, ahidden layer(s), and an output layer, the DNN may mean a neural networkstructure in which a plurality of hidden layers are disposed between theinput layer and the output layer. A machine learning scheme based on theDNN structure may be referred to as deep learning. The hidden layer maybe connected to the input layer, the output layer, or other hiddenlayer(s) through weight vectors.

In an exemplary embodiment of the communication system, a machinelearning apparatus including a machine learning structure may perform alearning operation of updating the weight vectors of the artificialneural network. The machine learning apparatus may include a multi-layerperceptron classifier. The learning operation of the artificial neuralnetwork may be performed by the multi-layer perceptron classifierincluded in the machine learning apparatus. The multi-layer perceptronclassifier may train the artificial neural network through apreconfigured learning algorithm. The learning algorithm may includemachine learning algorithms such as a supervised learning algorithm anda non-supervised learning algorithm.

In an exemplary embodiment of the communication system, the machinelearning apparatus may perform a series of operations throughfeed-forward operations in the artificial neural network structure andobtain an output value. The machine learning apparatus may calculateerror information based on the output value and a preset referencevalue. The machine learning apparatus may perform a learning operationof modifying the weight vectors between the layers of the artificialneural network by back-propagating the calculated error information. Themachine learning apparatus may modify the weight vectors between thelayers of the artificial neural network through a preconfiguredoptimization algorithm. For example, the optimization algorithm mayinclude a gradient descent scheme, alternating gradient descent scheme,stochastic gradient descent scheme, or adam-optimizer algorithm. Themachine learning apparatus may repeatedly perform the learning operationby the number of epochs corresponding to a preset number of learning. Asthe number of epochs increases, prediction performance or accuracy of amodel obtained through the machine learning may be improved. On theother hand, as the number of epochs increases, the amount of computationin the machine learning process may increase, the computation load mayincrease, and the learning efficiency may decrease. The number of epochsmay be set to a value that a person skilled in the art determines isappropriate to improve the performance of the machine learningapparatus.

In an exemplary embodiment of the communication system, the resourceallocation apparatus may perform pre-training for the resourceallocation operation based on the predetermined machine learningstructure. When the machine learning structure corresponds to an ANN,the total number of layers of the neural network structure may be L, andL may be a natural number of 2 or more. When the neural networkcorresponds to a DNN, L may be a natural number of 4 or more. Each layermay be expressed as the l(0,1, . . . L−1)-th layer from the input layerto the output layer, and among them, the (l=1)-th to (l=(L−2))-th layersmay be the hidden layers. For example, the DNN structure may includethree hidden layers, and the three hidden layers may consist of 32, 64,and 32 hidden nodes, respectively. Alternatively, the machine learningstructure may correspond to a perceptron structure that is a linearclassification machine learning tool. However, this is merely an examplefor convenience of description, and exemplary embodiments of thecommunication system are not limited thereto and may encompass variousexemplary embodiments of machine learning or artificial neural networktechnologies.

In an exemplary embodiment of the communication system, one or moremachine learning structures included in the resource allocationapparatus may receive input data I and generate output data O togenerate a trained model. Here, the operation of generating the trainedmodel may be performed at a specific point in time before the resourceallocation apparatus actually performs the resource allocation operationin the communication environment.

In an exemplary embodiment of the communication system, the resourceallocation apparatus may generate a first model through learning in afirst machine learning structure, and may generate a second modelthrough learning in a second machine learning structure. Here, the firstmodel may be generated through learning based on the previouslygenerated second model.

In the second machine learning structure included in the resourceallocation apparatus, the input data I may include information relatedto a per-beam (or per-cell) required traffic amount. For example, theinput data I may be vector data obtained by converting the informationon the per-beam (or per-cell) required traffic amount into a vector.Based on the input data I input to the second machine learning structurein a vector form, the output data O may be output. Here, the input dataI may be expressed as in Equation 1 below.

I=[(R _(b))₁ ¹(R _(b))₁ ² . . . (R _(b))₁ ^(M) . . . (R_(b))_(i) ^(j) .. . (R _(b))_(F) ^(M)]^(T)  [Table 1]

In Equation 1, F may correspond to the frequency reuse factor. M may bethe number of beams and/or cells. (R_(b))_(i) ^(j) may indicate per-beamrequired traffic amount. In the second machine learning structure, anoperation for finding values satisfying an objective function defined asin Equation 2 may be performed based on the input data I.

$\begin{matrix}{{{\underset{w_{i}}{argmin}P_{sum}},{{\sum\limits_{i = 1}^{F}w_{i}} \leq W},{w_{i} \geq 0},{P_{sum} \leq P_{\max}}}{{P_{sum} = {\sum\limits_{i}^{F}{\sum\limits_{j}^{M}\left( P_{t} \right)_{i}^{j}}}},{\left( P_{t} \right)_{i}^{j} = {f\left( \left( R_{b} \right)_{i}^{j} \right)}}}} & \left\lbrack {{Equation}2} \right\rbrack\end{matrix}$

In Equation 2, w_(i) may correspond to each bandwidth, and W maycorrespond to the total bandwidth available in a first communicationnode or communication system. P_(sum) may correspond to a sum of powervalues P_(t) for the respective bandwidths, and P_(max) may correspondto a sum of powers available in the first communication node orcommunication system. P_(t) may be defined as a value of a predeterminedfunction having R_(b) corresponding to the required traffic amount as avariable. In Equation 2,

$\arg\min\limits_{w_{i}}P_{sum}$

may be regarded as an equation of obtaining the bandwidth w_(i) thatminimizes the value of P_(sum).

The output data O may correspond to a resource allocation result to beobtained through the resource allocation operation. For example, theoutput data O may correspond to a bandwidth allocation result for eachbeam (or for each cell). That is, the second machine learning structuremay receive information related to the per-beam required traffic amountand output values corresponding to the bandwidth allocation amount. Theoutput data O may be expressed as in Equation 3 below.

O=[w ₁ w ₂ . . . w _(F)]^(T)  [Equation 3]

The output values included in the output data O of the second machinelearning structure may be calculated as in Equation 4 by using a weightmatrix W_(lin) trained according to a regression learning or a linearregression (LR) learning scheme.

O=W _(lin) t  [Equation 4]

In Equation 4, t may correspond to a vector obtained by adding a biasterm to the input data I of Equation 1. For example, it may be expressedas t=[1 I]. The weight matrix W_(lin) may be calculated as in Equation 5using an input learning data matrix X and an output learning data matrixY.

W _(lin)=(X ^(T) X)⁻¹ X ^(T) Y  [Equation 5]

Each row of the matrix X may be configured with t for learning, and eachrow of Y may become an output O corresponding to t. When the number oflearning data is N in total and the output of Equation 3 is derivedusing the input data vector of Equation 4, the size of the matrix X isN×(1+MF) and the size of the matrix Y is N×F.

As a result of the pre-training in the second machine learningstructure, the second model may be generated. For example, as a resultof the pre-training based on the data obtained from an iterative schemesuch as an exhaustive search scheme in the second machine learningstructure, the second model for bandwidth calculation based on therequired traffic amount information may be generated. In other words,the second model generated through the pre-training may output theoutput data O including bandwidth allocation information as shown inEquation 3 when the input data I including required traffic amountinformation is input as shown in Equation 1.

Meanwhile, in the first machine learning structure included in theresource allocation apparatus, the input data I may include informationrelated to the per-beam (or per-cell) required traffic amount. Here, theinput data I may be vector data obtained by converting the informationon the per-beam (or per-cell) required traffic amount into a vector. Inthe first machine learning structure, the input data I may be expressedin the same or similar manner to Equation 1.

The first machine learning structure may correspond to a perceptronstructure. The first machine learning structure may output a value of +1or −1 as an output value. Based on the second model generated throughthe pre-training in the second machine learning structure, the firstmachine learning structure may perform pre-training for determiningwhether the required traffic amount included in the input data I can benormally serviced through resources such as a system bandwidth or poweravailable in the first communication node. For example, the firstmachine learning structure may be trained so as to output +1 when therequired traffic amount included in the input data I can be servicedwhile satisfying a first condition as in Equation 6 below, and output −1when the required traffic amount included in the input data I cannot beserviced while satisfying the first condition as in Equation 6 below.

$\begin{matrix}{{{\sum\limits_{i = 1}^{F}w_{i}} \leq W},{w_{i} \geq 0},{P_{sum} \leq P_{\max}},{\gamma_{i}^{j} \geq 0},{\forall i},j} & \left\lbrack {{Equation}6} \right\rbrack\end{matrix}$

In Equation 6, w_(i) may correspond to each bandwidth, and W maycorrespond to a total bandwidth available in the first communicationnode or communication system. P_(sum) may correspond to a sum of powervalues for the respective bandwidths corresponding to the input data I,and P_(max) may correspond to a sum of power available in the firstcommunication node or communication system. γ_(i) ^(j) may be a valuecorresponding to a signal-to-noise ratio (SNR) condition for each beam,and may be calculated as in Equation 7 below.

$\begin{matrix}{{{\Delta_{i}\left\lbrack {\gamma_{i}^{1}\gamma_{i}^{2}\ \ldots\gamma_{i}^{M}} \right\rbrack}^{T} = \left\lbrack {11\ldots\ 1} \right\rbrack^{T}},{{{where}\Delta_{i}} = \begin{bmatrix}{1/\rho_{i}^{1}} & {{- g_{i}^{2,1}}\eta_{i}^{1}} & \ldots & {{- g_{i}^{M,1}}\eta_{i}^{M}} \\{{- g_{i}^{1,2}}\eta_{i}^{1}} & {1/\rho_{i}^{1}} & \ldots & {{- g_{i}^{M,2}}\eta_{i}^{M}} \\ \vdots & \vdots & \ddots & \vdots \\{{- g_{i}^{1,M}}\eta_{i}^{1}} & {{- g_{i}^{2,M}}\eta_{i}^{2}} & \ldots & {1/\rho_{i}^{M}}\end{bmatrix}}} & \left\lbrack {{Equation}7} \right\rbrack\end{matrix}$

In Equation 7, g_(i) ^(k,j) may mean a relative gain ratio of a pathfrom the k-th beam or cell to the j-th beam or cell using f_(i). η_(i)^(j) may mean a spectral efficiency calculated as (R_(b))_(i)^(j)/w_(i). ρ_(i) ^(j) may mean a ratio of signal-to-noise plusinterference that allows the spectral efficiency to be η_(i) ^(j) whilesatisfying the error performance required in the system. Here, ρ_(i)^(j) may be obtained based on the Shannon's capacity limit theorem, ormay be obtained in advance through simulation. When γ_(i) ^(j)calculated based on Equation 7 or the like is equal to or greater than0, it may be determined that the SNR condition for the correspondingbeam is satisfied.

As a result of the pre-training in the first machine learning structure,the first model may be generated. For example, as a result of thepre-training based on the iterative scheme such as an exhaustive searchscheme in the first machine learning structure, the first model fordetermining whether the first condition is satisfied based on therequired traffic amount information may be generated. In other words,when the input data I including the required traffic amount informationas shown in Equation 1 is input, the first model generated through thepre-training may output +1 when the required traffic amount can beserviced while satisfying the first condition, and may output −1 whenthe required traffic amount cannot be serviced while satisfying thefirst condition. However, this is merely an example for convenience ofdescription, and the determination method and output value of the firstmodel may be determined in various ways.

The resource allocation apparatus of the first communication node mayperform resource allocation operations based on the required trafficamount information in the communication environment by using the firstand second models generated through the first and second machinelearning structures. The resource allocation apparatus and the resourceallocation method will be described in more detail below with referenceto FIGS. 6 and 7 .

FIG. 6 is a conceptual diagram for describing an exemplary embodiment ofa resource allocation apparatus in a communication system.

Referring to FIG. 6 , in an exemplary embodiment of the communicationsystem, a resource allocation apparatus 600 may perform an operation ofallocating resources such as bandwidth and/or power based on requiredtraffic information or required traffic amount information. The resourceallocation apparatus 600 may include the same or similar components asthose shown in FIG. 6 for the resource allocation operation. Theresource allocation apparatus 600 may include a vector conversion unit610, a possibility determination unit 620, a bandwidth allocation unit630, a required amount adjustment unit 640, a power allocation unit 650,a boundary value adjustment unit 660, and the like.

In an exemplary embodiment of the communication system, the resourceallocation apparatus 600 may obtain required traffic amount information(R₁ ¹, R₁ ², . . . , R₁ ^(M), . . . , R_(i) ^(j), . . . , R_(F) ^(M)).The vector conversion unit 610 may convert the required traffic amountinformation input to the resource allocation apparatus 600 to generateinput data t in form of a vector. The input data t of the vectorconversion unit 610 may be the same as or similar to the input data I ofEquation 1. The input data t may be the same as or similar to Equation 8below.

t=[1I]=[1, R ₁ ¹ , R ₁ ² , . . . , R ₁ ^(M) , . . . , R _(i) ^(j) , . .. , R _(F) ^(M)]^(T)  [Equation 8]

The input data t in form of a vector generated by the vector conversionunit 610 may be input to the possibility determination unit 620. Thepossibility determination unit 620 may determine whether services can beprovided based on the input data t by using the pre-trained first model.The possibility determination unit 620 may perform calculation relatedto whether services can be provided based on the input data t by thefirst model generated through the pre-training in the first machinelearning structure having the perceptron structure described withreference to FIG. 5 . The possibility determination unit 620 may output+1 when the required traffic amount included in the input data t can beserviced while satisfying the first condition, and output −1 when therequired traffic amount included in the input data t cannot be servicedwhile satisfying the first condition. The calculation result in thefirst model may be expressed as Equation 9 below.

y=sgn(f(t))=sgn(c ^(T) t)  [Equation 9]

In Equation 9, the sgn function may correspond to a sign function or asignum function, c may correspond to a coefficient vector correspondingto the pre-trained first model and correspond to a coefficient vectorsetting a boundary value for determining a sign for f(t). Through thecalculation shown in Equation 9, the first model may output a value of+1 or a value of −1.

When a value of +1 is output from the possibility determination unit620, the value of +1 may be input to the bandwidth allocation unit 630as a command. The bandwidth allocation unit 630 may allocate a bandwidthaccording to the input data t transferred from the vector conversionunit 610 according to the pre-trained first model. The bandwidthallocation unit 630 may perform an operation for bandwidth allocationaccording to the input data t by the second model generated throughpre-training in the second machine learning structure to which thelinear regression scheme described with reference to FIG. 5 is applied.When the input data t including the required traffic amount information(e.g., t=[1, R₁ ¹, R₁ ², . . . , R₁ ^(M), . . . , R_(i) ^(j), . . . ,R_(F) ^(M)]^(T)) is input, the second model of the bandwidth allocationunit 630 may output the output data O including bandwidth allocationinformation. Here, the output data O may be the same as or similar toEquation 3 (O=[w₁ w₂ . . . w_(F)]^(T)). When the coefficient matrixcorresponding to the pre-trained second model is W_(lin), the outputdata O may be expressed as in Equation 10 below.

O=[w ₁ w ₂ . . . w _(F)]^(T) =W _(lin) t  [Equation 10]

On the other hand, when a value of −1 is output from the possibilitydetermination unit 620, the value of −1 may be input to the requiredamount adjustment unit 640 as a command. That the value of −1 is inputto the required amount adjustment unit 640 may mean that it isdetermined that the required traffic amount included in the input data Icannot be serviced while satisfying the first condition. In this case,the required amount adjustment unit 640 may generate adjusted input dataI′ by adjusting the input data I. The adjusted input data I′ may beconfigured to have reduced values from the previous required trafficamount included in the input data I.

The operation of generating the adjusted input data I′ in the requiredamount adjustment unit 640 may be performed through an affineprojection. The required amount adjustment unit 640 may generate theadjusted input data I′ by performing affine projection of the input dataI to a boundary space of the perceptron. To express this mathematically,the required amount adjustment unit 640 may perform affine projection ofthe input data I to a null space of f(t) in the process of calculatingthe second model having the perceptron structure. Here, the null spaceof f(t) may mean a solution space of f(t)=0. The solution space off(t)=0 may be defined by a vector equation as in Equation 11.

U=V+a=s ₁ v ₁ +s ₂ v ₂ + . . . +s _(FM−1) +a  [Equation 11]

In Equation 11, V may correspond to a space defined as V=span{v₁, v₂, .. . , v_(FM−1)}, and s_(j)(j=1, 2, . . . , FM−1) may be an arbitraryreal number, and a may correspond to a vector for transitioning thespace V to a space U. The operation of generating the adjusted inputdata I′ by performing affine projection of the input data I to thesolution space of f(t)=0, performed by the required amount adjustmentunit 640, may be expressed as Equation 12 below.

I′=Proj_(u)(I)=Proj_(v)(I−a)+a=A(A ^(T) A)⁻¹ A ^(T)(I−a)+a  [Equation12]

In Equation 12, matrix A may be defined as A=[v₁ v₂ . . . v_(FM−1)],having v_(j) in Equation 11 as column vectors. Such the affineprojection may be regarded as an operation for adding a minimum reducedamount within a resource limit range that can be provided by the systemwhen the required traffic amount cannot be serviced while satisfying aninterference condition and a resource limit that can be provided by thesystem.

The required amount adjustment unit 640 may output the adjusted inputdata t′=[1 I′] including the reduced required traffic amountinformation. The adjusted input data t′ may be input to the bandwidthallocation unit 630. The bandwidth allocation unit 630 may allocate abandwidth according to the input data t′ adjusted by the pre-trainedsecond model. The bandwidth allocation unit 630 may output the outputdata O corresponding to the adjusted input data t′ by performing theoperation shown in Equation 13.

O=[w ₁ w ₂ . . . w _(F)]^(T) =W _(lin) t′  [Equation 13]

The bandwidth allocation unit 630 may output the output data O obtainedbased on the input data t as shown in Equation 10, or the output data Oobtained based on the input data t′ as shown in Equation 13. The outputdata O may be input to the power allocation unit 650. The powerallocation unit 650 may perform an operation for allocating a powerbased on the output data O including the bandwidth allocationinformation. The power allocation unit 650 may calculate per-beam (orper-cell) power allocation information (P_(t))₁ ¹, (P_(t))₁ ², . . . ,(P_(t))₁ ^(M), . . . , (P_(t))_(i) ^(j), . . . , (P_(t))_(F) ^(M) basedon the per-beam (or per-cell) bandwidth allocation information includedin the output data O.

The power allocation unit 650 may calculate γ_(i) ^(j) valuescorresponding to the SNR according to the bandwidth allocationinformation generated by the bandwidth allocation unit 630 and/or thepower allocation information generated by the power allocation unit 650.The power allocation unit 650 may identify whether the calculated valuesof γ_(i) ^(j) satisfy the SNR condition (γ_(i) ^(j)≥0, ∀i, j) accordingto the first condition. Here, γ_(i) ^(j) values may be calculated basedon the same or similar method to Equation 7.

When the SNR condition according to the first condition is satisfied,the resource allocation apparatus 600 may determine that the bandwidthallocation information (w₁, w₂, . . . , w_(F)) generated by thebandwidth allocation unit 630 and the power allocation information((P^(T))₁ ¹, (P_(t))₁ ², . . . , (P_(t))₁ ^(M), . . . , (P_(t))_(F)^(M)) generated by the power allocation unit 650 are obtained as aresource allocation result. The resource allocation apparatus 600 mayoutput the bandwidth allocation information and the power allocationinformation.

On the other hand, when the SNR condition according to the firstcondition is not satisfied, a command indicating that the SNR conditionis not satisfied may be transmitted to the boundary value adjustmentunit 660. The boundary value adjustment unit 660 may perform anoperation for adjusting a boundary value used for the affine projectionby the required amount adjustment unit 640. Specifically, the requiredamount adjustment unit 640 may generate an adjusted vector a′ byadjusting the vector a used in the affine projection as in Equation 12.By the adjusted vector a′, U in Equation 11 may be adjusted to U′ inEquation 14.

U′=V+a′=s ₁ v ₁ +s ₂ v ₂ + . . . +s _(FM−1) V _(FM−1) +a′  [Equation 14]

U′ in Equation 14 may be regarded as having a boundary space shifted ina negative direction from a perceptron boundary space of U in Equation11. That is, the adjusted vector a′ may be adjusted to a value such thatthe boundary line of U′ is moved in a negative direction from theboundary space of U′. The vector a′ adjusted in this manner may betransmitted to the required amount adjustment unit 640. The requiredamount adjustment unit 640 may perform the affine projection based onthe adjusted vector a′ and the adjusted space U′ by performing anoperation as in Equation 15 that is a modified from of Equation 12.

I′=Proj_(u′)(I)=Proj_(v)(I−a′)+a′=A(A ^(T) A)⁻¹ A^(T)(I−a′)+a′  [Equation 15]

The required amount adjustment unit 640 may output the adjusted inputdata I′ obtained based on Equation 15. The adjusted input data I′ may beinput to the bandwidth allocation unit 630. When the adjusted input datat′=[1 I′] generated based on the boundary value adjusted by the boundaryvalue adjustment unit 660 is input to the bandwidth allocation unit 630,the bandwidth allocation operation in the bandwidth allocation unit 630and the power allocation operation in the power allocation unit 650 maybe performed again. When it is determined that the SNR conditionaccording to the first condition is satisfied according to there-performed bandwidth allocation operation and power allocationoperation, the resource allocation apparatus 600 may output bandwidthallocation information and power allocation information.

FIG. 7 is a conceptual diagram for describing an exemplary embodiment ofa resource allocation method in a communication system.

Referring to FIG. 7 , in an exemplary embodiment of a communicationsystem, a first communication node or a resource allocation apparatusincluded in the first communication node may perform an operation ofallocating resources such as bandwidth and/or power based on requiredtraffic information or required traffic amount information. Here, theresource allocation apparatus may be the same as or similar to theresource allocation apparatus 600 described with reference to FIG. 6 .Alternatively, the first communication node performing resourceallocation in the communication system may be referred to as a resourceallocation apparatus. Hereinafter, in describing an exemplary embodimentof the resource allocation method in the communication system withreference to FIG. 7 , content overlapping with that described withreference to FIGS. 1 to 6 may be omitted.

In an exemplary embodiment of the communication system, the resourceallocation apparatus may obtain per-beam required traffic amountinformation (S700). Here, the required traffic amount information may bethe same as or similar to the required traffic amount informationdescribed with reference to FIG. 6 . The resource allocation apparatusmay convert the obtained required traffic amount information into avector to generate input data t=[1 I] in form of a vector (S710). Theoperation of the resource allocation apparatus according to the stepS710 may be the same as or similar to that of the vector conversion unit610 described with reference to FIG. 6 .

The resource allocation apparatus may input the input data t obtained inthe step S710 to the first model generated through pre-training based onthe perceptron structure, and identify a calculation result in the firstmodel (S720). The operation of the resource allocation apparatusaccording to the step S720 may be the same as or similar to theoperation of the possibility determination unit 620 described withreference to FIG. 6 .

When the result of the calculation based on the first model is +1, theresource allocation apparatus may input the input data t to the secondmodel generated through pre-training based on the linear regressionscheme, and may calculate bandwidth allocation information through thecalculation based on the second model (S730). The operation of theresource allocation unit according to the step S730 may be the same asor similar to that of the bandwidth allocation unit 630 described withreference to FIG. 6 .

On the other hand, when the result of the calculation based on the firstmodel is −1, the resource allocation apparatus may generate the adjustedinput data t′ by adjusting the input data t through the affineprojection (S740). The adjusted input data t′ may be configured to havereduced values from the previous required traffic amount included in theinput data t. The operation of the resource allocation apparatusaccording to the step S740 may be the same as or similar to theoperation of the required amount adjustment unit 640 described withreference to FIG. 6 . The resource allocation apparatus may input theadjusted input data t′ to the second model, and may calculate bandwidthallocation information through calculation based on the second model(S730).

The resource allocation apparatus may output the output data O throughthe operation according to the step S730. The output data O may includeper-beam bandwidth allocation information. The resource allocationapparatus may calculate per-beam power allocation information based onthe per-beam bandwidth allocation information (S750). The resourceallocation apparatus may identify whether the bandwidth allocationinformation obtained through the step S730 and/or power allocationinformation obtained through the step S750 satisfies the SNR condition(S760). The operations of the resource allocation apparatus according tothe steps S750 and S760 may be the same as or similar to those of thebandwidth allocation unit and the power allocation unit 650 describedwith reference to FIG. 6 .

When it is determined that the SNR condition is satisfied, the resourceallocation apparatus may regard the obtained per-beam bandwidthallocation information and/or per-beam power allocation information as aresource allocation result. The resource allocation apparatus 600 maycomplete the resource allocation operation for each beam by outputtingthe bandwidth allocation information and the power allocationinformation (S780).

On the other hand, when it is determined that the SNR condition is notsatisfied, the resource allocation apparatus may perform an operationfor adjusting the boundary value used for the affine projection in thestep S740 (S770). The operation of the resource allocation apparatusaccording to the step S770 may be the same as or similar to theoperation of the boundary value adjustment unit 660 described withreference to FIG. 6 . The resource allocation apparatus may perform theoperation of the step S740 again through the affine projection based onthe boundary value adjusted in the step S770. The resource allocationapparatus may perform the operations according to the steps S730 andS750 to S780 based on the adjusted input data obtained through the stepsS770 and S740.

According to exemplary embodiments of a method and an apparatus forresource allocation in a wireless communication system, in the wirelesscommunication system to which the frequency reuse technique is applied,a first communication node may allocate resources such as bandwidth andpower based on machine learning. The first communication node may obtaininformation on per-beam (or per-cell) required traffic amount, anddetermine whether the required traffic amount can be serviced throughresources such as a system bandwidth and a power available in the firstcommunication node. When it is determined that the required trafficamount cannot be serviced, the traffic amount may be reduced, and aper-beam bandwidth and/or power for servicing the reduced traffic amountmay be determined. Through such the process, an unnecessary amount ofcomputation in the resource allocation process can be reduced, andservice efficiency can be improved.

However, the effects that can be achieved by the resource allocationmethod and apparatus in the wireless communication system according tothe exemplary embodiments of the present disclosure are not limited tothose mentioned above, and other effects not mentioned may be clearlyunderstood by those of ordinary skill in the art to which the presentdisclosure belongs from the configurations described in the presentdisclosure.

The exemplary embodiments of the present disclosure may be implementedas program instructions executable by a variety of computers andrecorded on a computer readable medium. The computer readable medium mayinclude a program instruction, a data file, a data structure, or acombination thereof. The program instructions recorded on the computerreadable medium may be designed and configured specifically for thepresent disclosure or can be publicly known and available to those whoare skilled in the field of computer software.

Examples of the computer readable medium may include a hardware devicesuch as ROM, RAM, and flash memory, which are specifically configured tostore and execute the program instructions. Examples of the programinstructions include machine codes made by, for example, a compiler, aswell as high-level language codes executable by a computer, using aninterpreter. The above exemplary hardware device can be configured tooperate as at least one software module in order to perform theembodiments of the present disclosure, and vice versa.

While the embodiments of the present disclosure and their advantageshave been described in detail, it should be understood that variouschanges, substitutions and alterations may be made herein withoutdeparting from the scope of the present disclosure.

What is claimed is:
 1. An operation method of a first apparatus in acommunication system, the operation method comprising: obtaininginformation on a per-beam required traffic amount; determining whetherthe per-beam required traffic amount can be serviced while satisfying afirst condition including an available total bandwidth condition and anavailable total power condition according to a first model generatedthrough pre-training in a first machine learning structure; in responseto determining that the per-beam required traffic amount can be servicedwhile satisfying the first condition, calculating per-beam bandwidthallocation information based on the per-beam required traffic amount;calculating per-beam power allocation information based on the per-beambandwidth allocation information; identifying whether a signal-to-noiseratio (SNR) condition included in the first condition is satisfied basedon the per-beam power allocation information; and in response toidentifying that the SNR condition is satisfied, outputting the per-beambandwidth allocation information and the per-beam power allocationinformation.
 2. The operation method according to claim 1, wherein thedetermining comprises: generating first input data by converting theinformation on the per-beam required traffic amount into a vector;inputting the first input data to the first model; and identifying anoutput value of the first model.
 3. The operation method according toclaim 2, wherein the first machine learning structure has a perceptronstructure, the output value of the first model, which has a positivevalue, means that the per-beam required traffic amount can be servicedwhile satisfying the first condition, and the output value of the firstmodel, which has a negative value, means that the per-beam requiredtraffic amount cannot be serviced while satisfying the first condition.4. The operation method according to claim 2, wherein the pre-trainingin the first machine learning structure is performed based on a secondmodel after the second model is generated through pre-training in asecond machine learning structure for calculation of the per-beambandwidth allocation information.
 5. The operation method according toclaim 1, wherein the calculating of the per-beam bandwidth allocationinformation comprises: inputting first input data generated based on theinformation on the per-beam required traffic amount into a second modelgenerated through pre-training in a second machine learning structure;and obtaining output data output from the second model, wherein theoutput data includes the per-beam bandwidth allocation information. 6.The operation method according to claim 5, wherein the second machinelearning structure has a machine learning structure according to alinear regression learning scheme, and the pre-training in the secondmachine learning structure is performed based on an exhaustive searchscheme in a direction in which a value of a loss function calculatedbased on first bandwidth allocation information output based oninformation on a first required traffic amount is minimized.
 7. Theoperation method according to claim 1, further comprising, after thedetermining, in response to determining that the per-beam requiredtraffic amount cannot be serviced while satisfying the first condition,performing an affine projection operation for calculating a reducedrequired traffic amount reduced from the per-beam required trafficamount; and calculating the per-beam bandwidth allocation informationbased on second input data generated as a result of the affineprojection operation and including information on the reduced requiredtraffic amount.
 8. The operation method according to claim 7, furthercomprising, after the identifying, in response to identifying that theSNR condition is not satisfied, adjusting a boundary value used in theaffine projection operation; performing the affine projection operationbased on the adjusted boundary value; and calculating the per-beambandwidth allocation information based on third input data generated asa result of the affine projection operation performed based on theadjusted boundary value.
 9. A first apparatus in a communication system,comprising: a processor; wherein the processor causes the firstapparatus to: obtain information on a per-beam required traffic amount;determine whether the per-beam required traffic amount can be servicedwhile satisfying a first condition including an available totalbandwidth condition and an available total power condition according toa first model generated through pre-training in a first machine learningstructure; in response to determining that the per-beam required trafficamount can be serviced while satisfying the first condition, calculateper-beam bandwidth allocation information based on the per-beam requiredtraffic amount; calculate per-beam power allocation information based onthe per-beam bandwidth allocation information; identify whether asignal-to-noise ratio (SNR) condition included in the first condition issatisfied based on the per-beam power allocation information; and inresponse to identifying that the SNR condition is satisfied, output theper-beam bandwidth allocation information and the per-beam powerallocation information.
 10. The first device according to claim 9,wherein in the determining, the processor further causes the firstapparatus to: generate first input data by converting the information onthe per-beam required traffic amount into a vector; input the firstinput data to the first model; and identify an output value of the firstmodel.
 11. The first device according to claim 10, wherein the firstmachine learning structure has a perceptron structure, the output valueof the first model, which has a positive value, means that the per-beamrequired traffic amount can be serviced while satisfying the firstcondition, the output value of the first model, which has a negativevalue, means that the per-beam required traffic amount cannot beserviced while satisfying the first condition, and the pre-training inthe first machine learning structure is performed based on a secondmodel after the second model is generated through pre-training in asecond machine learning structure for calculation of the per-beambandwidth allocation information.
 12. The first device according toclaim 9, wherein in the calculating of the per-beam bandwidth allocationinformation, the processor further causes the first apparatus to: inputfirst input data generated based on the information on the per-beamrequired traffic amount into a second model generated throughpre-training in a second machine learning structure; and obtain outputdata output from the second model, wherein the output data includes theper-beam bandwidth allocation information.
 13. The first deviceaccording to claim 12, wherein the second machine learning structure hasa machine learning structure according to a linear regression learningscheme, and the pre-training in the second machine learning structure isperformed based on an exhaustive search scheme in a direction in which avalue of a loss function calculated based on first bandwidth allocationinformation output based on information on a first required trafficamount is minimized.
 14. The first device according to claim 9, whereinthe processor further causes the first apparatus to, after thedetermining, in response to determining that the per-beam requiredtraffic amount cannot be serviced while satisfying the first condition,perform an affine projection operation for calculating a reducedrequired traffic amount reduced from the per-beam required trafficamount; and calculate the per-beam bandwidth allocation informationbased on second input data generated as a result of the affineprojection operation.
 15. The first device according to claim 14,wherein the processor further causes the first apparatus to, after theidentifying, in response to identifying that the SNR condition is notsatisfied, adjust a boundary value used in the affine projectionoperation; perform the affine projection operation based on the adjustedboundary value; and calculate the per-beam bandwidth allocationinformation based on third input data generated as a result of theaffine projection operation performed based on the adjusted boundaryvalue.